Bayesian Probabilistic Projections of Life Expectancy for All Countries
 Adrian E. Raftery,
 Jennifer L. Chunn,
 Patrick Gerland,
 Hana Ševčíková
 … show all 4 hide
Abstract
We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we conducted an outofsample crossvalidation experiment, fitting the model to the data from 1950–1995 and using the estimated model to forecast for the subsequent 10 years. The 10year predictions had a mean absolute error of about 1 year, about 40 % less than the current UN methodology. The probabilistic forecasts were calibrated in the sense that, for example, the 80 % prediction intervals contained the truth about 80 % of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free, publicly available R software packages called bayesLife and bayesDem are available to implement the method.
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 Title
 Bayesian Probabilistic Projections of Life Expectancy for All Countries
 Open Access
 Available under Open Access This content is freely available online to anyone, anywhere at any time.
 Journal

Demography
Volume 50, Issue 3 , pp 777801
 Cover Date
 20130601
 DOI
 10.1007/s135240120193x
 Print ISSN
 00703370
 Online ISSN
 15337790
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Bayesian hierarchical model
 Double logistic function
 LeeCarter model
 Life expectancy at birth
 Markov chain Monte Carlo
 Industry Sectors
 Authors

 Adrian E. Raftery ^{(1)}
 Jennifer L. Chunn ^{(2)}
 Patrick Gerland ^{(3)}
 Hana Ševčíková ^{(4)}
 Author Affiliations

 1. Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA
 2. Mathematics and Statistics Help Center, James Cook University, Singapore, Singapore
 3. United Nations Population Division, Population Estimates and Projection Section, New York, NY, USA
 4. Center for Statistics and the Social Sciences, University of Washington, Seattle, WA, USA